Deep learning for dynamic modeling and coded information storage of vector-soliton pulsations in mode-locked fiber lasers

TL;DR

This paper introduces a deep learning approach using bidirectional LSTM neural networks to efficiently model and predict the complex dynamics of vector-soliton pulsations in mode-locked fiber lasers, with applications in real-time control and information storage.

Contribution

It presents a novel TP-Bi_LSTM RNN model for predicting soliton pulsations and implementing coded information storage, improving efficiency over traditional numerical methods.

Findings

Accurate real-time prediction of vector-soliton pulsations.

Successful demonstration of coded information storage using the RNN.

Potential applications in ultrafast optics and optical information storage.

Abstract

Soliton pulsations are ubiquitous feature of non-stationary soliton dynamics in mode-locked lasers and many other physical systems. To overcome difficulties related to huge amount of necessary computations and low efficiency of traditional numerical methods in modeling the evolution of non-stationary solitons, we propose a two-parallel bidirectional long short-term memory recurrent neural network, with the main objective to predict dynamics of vector-soliton pulsations in various complex states, whose real-time dynamics is verified by experiments. Besides, the scheme of coded information storage based on the TP-Bi_LSTM RNN, instead of actual pulse signals, is realized too. The findings offer new applications of deep learning to ultrafast optics and information storage.